A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Abstract

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is hyperspectral imaging (HSI). Unlike multispectral images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or super resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This article reviews and discusses the most important algorithms relevant to this area of research between 2002 and 2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Metaanalysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

Keywords

• Convolutional neural networks (CNNs)
• Fusion
• hyperspectral
• literature review
• remote sensing
• single image super resolution (SISR)